Improving the precision of example-based machine translation by learning from user feedback

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buir.advisorÇiçekli, İlyas
dc.contributor.authorDaybelge, Turhan Osman
dc.date.accessioned2016-01-08T18:02:05Z
dc.date.available2016-01-08T18:02:05Z
dc.date.issued2007
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionAnkara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2007.en_US
dc.descriptionIncludes bibliographical references leaves 110-113en_US
dc.description.abstractExample-Based Machine Translation (EBMT) is a corpus based approach to Machine Translation (MT), that utilizes the translation by analogy concept. In our EBMT system, translation templates are extracted automatically from bilingual aligned corpora, by substituting the similarities and differences in pairs of translation examples with variables. As this process is done on the lexical-level forms of the translation examples, and words in natural language texts are often morphologically ambiguous, a need for morphological disambiguation arises. Therefore, we present here a rule-based morphological disambiguator for Turkish. In earlier versions of the discussed system, the translation results were solely ranked using confidence factors of the translation templates. In this study, however, we introduce an improved ranking mechanism that dynamically learns from user feedback. When a user, such as a professional human translator, submits his evaluation of the generated translation results, the system learns “contextdependent co-occurrence rules” from this feedback. The newly learned rules are later consulted, while ranking the results of the following translations. Through successive translation-evaluation cycles, we expect that the output of the ranking mechanism complies better with user expectations, listing the more preferred results in higher ranks. The evaluation of our ranking method, using the precision value at top 1, 3 and 5 results and the BLEU metric, is also presented.en_US
dc.description.degreeM.S.en_US
dc.description.statementofresponsibilityDaybelge, Turhan Osmanen_US
dc.format.extentxiv, 144 leaves, ill, tablesen_US
dc.identifier.itemidBILKUTUPB103951
dc.identifier.urihttp://hdl.handle.net/11693/14558
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectExample-Based Machine Translationen_US
dc.subjectLearning from User Feedbacken_US
dc.subjectMorphological Disambiguationen_US
dc.subject.lccP309 .D39 2007en_US
dc.subject.lcshMachine translating.en_US
dc.subject.lcshComputational linguistic.en_US
dc.titleImproving the precision of example-based machine translation by learning from user feedbacken_US
dc.typeThesisen_US
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